The fact is, while we often think of ‘network analysis’ in terms of ‘social network analysis’, because it’s the most commonly known these days, network analysis is suitable for gaining insight into any kind of data that is in some way relational - that is, the pieces of data can be said to be in some way interrelated. Firstly, networks do not have to be based on real people. Martin LeGrandJean has visualised Shakespeare plays as social networks, drawing links between the characters every time they appear in the same scene together. The resulting networks helps us to understand how the characters are interrelated, and using a measurement known as density, are able to give an overall sense of the complexity of each play. LeGrandjean showed that a play like Hamlet, though longer, was less structurally complex than King Lear, for example.
I encourage you to think outside the box, and consider how your own area of interest, perhaps a disseration topic or essay, could be represented as a network. For example, researchers have constructed networks of words which co-occur in the same novel together. The resulting network can be analysed in the same way as any other network, looking for the most centrally-placed words, or looking for clusters of words which appear together often. This might highlight key themes or subjects within a novel, or help to understand the writing style of an author. The connections might be very abstract: the similarity between two paintings in terms of colour, for example, or what is known as a ‘line of sight’ network in archaeology, where a link is drawn if one settlement can directly see another.